BIC: Collaborative Research: Evolutionary Optimization of Biological Circuits: Towards Cellular Programming

重新设计甚至构建全新生物实体的能力为利用生物学解决人类问题提供了革命性的机会。然而，这个梦想远远超过了现实：设计新的生物机器，为细胞行为编程，建立新的生命形式，这些都构成了巨大的技术和概念挑战。我们的研究旨在通过开发普遍适用的工程方法来构建合成基因网络，从而迈出重要的第一步。在生物系统中实现即使是最简单的电路也需要对大量难以理解的参数进行繁琐的优化，其中许多参数既不能测量也不容易操作。模拟有时可以指导优化，但我们相信，生物系统最好的优化使用自然的编辑策略，进化。我们相信，基于计算预测的理性设计与定向进化相结合的方法——在实验室中制造突变，选择那些表现出所需行为的生物体——将是合成生物学进步的基础。实际上，我们将学习如何“培育”有用的合成基因网络，就像我们学会了如何培育有用的植物和动物一样。这种方法模仿自然进化，探索一组分子构成工程调控途径的巨大而复杂的功能景观。重要的是，它避免了我们对DNA序列如何编码一组特定生物功能的近乎完全的无知，这是任何“理性”设计方法所需要的详细理解。通过将随机突变引入DNA并筛选突变回路可能表达的不同功能，我们可以确定哪些功能是可能的，以及特定搜索过程可用的功能范围（例如针对特定基因的随机点突变）。通过进一步的分析，例如测序以确定突变和电路元件的生化分析，我们可以深入了解实现或修改整体功能的分子机制。在这个项目中，我们有三个具体目标。首先是验证一个“选择模块”，通过它我们可以在实验室中有效地进化组件和电路。这个模块将适当的电路功能与表达它的细胞的生存和生长能力联系起来。具有功能回路的细胞存活并生长；那些没有解决问题的国家没有。为了编程复杂的行为，我们还需要组件响应预定义的输入参数范围和可预测的输出参数。因此，我们的第二个目标是利用定向进化来创造一系列基于已被充分表征的框架蛋白LuxR的转录激活剂。实验室进化的LuxR变体将激活DNA上不同的非天然启动子位点的基因转录。最后，我们建议通过进化探索来研究可用于预定义组件集的电路功能范围。具体而言，我们将开发一系列“波段检测”电路，以响应预先指定的酰基- hsl浓度范围。这些电路将用于构建在固相中形成基因表达模式的合成系统。我们的最终目标是为合成生物学开发一种基本的使能技术，以及为计算开发生物启发模式和架构。我们设想，进化电路和由它们构建的合成多细胞系统将有助于研究人员开发基因调控、群体感应和细胞计算其他方面的定量模型。

The ability to redesign and even build completely new biological entities offers revolutionary opportunities for using biology to solve human problems. The dream, however, far outstrips the reality: engineering new biological machines, programming cells behaviors, and building new forms of life pose huge technical and conceptual challenges. Our research is designed to make some important first steps, by developing generally-applicable engineering methods for building synthetic gene networks. Implementation of even the most simple circuits in a biological system requires tedious optimization of a large number of poorly-understood parameters, many of which can neither be measured nor easily manipulated. Simulations can sometimes guide optimization, but we believe that biological systems are best optimized using nature's editing strategy, evolution. We believe that a combined approach of rational design based on computational predictions coupled with directed evolution-making mutations in the laboratory and selecting those organisms exhibiting the desired behaviors--will be fundamental to the progress of synthetic biology. We will in effect learn how to 'breed' useful synthetic gene networks, just as we have learned how to breed useful plants and animals.This approach mimics natural evolution in exploring the vast and complex landscape of functions available to a set of molecules making up an engineered regulatory pathway. Importantly, it circumvents our near-complete ignorance of how a DNA sequence encodes a specific set of biological functions, a detailed understanding that is required for any 'rational' design approach. By introducing random mutations into the DNA and screening for different functions that might be expressed by the mutant circuits, we can identify which functions are possible as well as the ranges of function available to the specific search process (e.g. random point mutation targeted to a specific gene). With further analysis, e.g. sequencing to identify the mutations and biochemical analysis of circuit components, we gain insights into the molecular mechanisms by which the overall function is achieved or modified. In this project we have three specific aims. The first is to validate a 'selection module' by which we can efficiently evolve components and circuits in the laboratory. This module connects proper circuit function to the ability of cells that express it to survive and grow. Cells with functioning circuits survive and grow; those that have not solved the problem do not. To program complex behaviors, we will also need components that respond to predefined ranges of input parameters with predictable output parameters. Thus our second aim is to use directed evolution to create a range of transcriptional activators based on the well-characterized framework protein, LuxR. Laboratory-evolved LuxR variants will activate gene transcription at different, nonnatural promoter sites on the DNA. Finally, we propose to investigate the range of circuit functions available to a predefined set of components via evolutionary exploration. Specifically, we will evolve a series of 'band detect' circuits that respond to a prespecified range of acyl-HSL concentrations. These circuits will be used to construct synthetic systems that form patterns of gene expression in the solid phase.Our ultimate goal is to develop a fundamental enabling technology for synthetic biology as well as for developing bio-inspired modes and architectures for computing. We envision that evolved circuits and the synthetic multicellular systems that can be constructed from them will be useful to researchers developing quantitative models of gene regulation, quorum sensing, and other aspects of cellular computing.